Triple
T5133787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old State, War, and Navy Building |
E115766
|
entity |
| Predicate | hasPavilions |
P12264
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Old State, War, and Navy Building, hasPavilions, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPavilions Context triple: [Old State, War, and Navy Building, hasPavilions, true]
-
A.
hasPavilion
chosen
Indicates that one entity possesses, includes, or is associated with a pavilion as part of its structure, property, or facilities.
-
B.
numberOfPavilions
Indicates the total count of pavilions associated with a given entity or context.
-
C.
hasPavilionFunction
Indicates that something serves the role or function of a pavilion, such as providing a designated space or facility for specific activities or purposes.
-
D.
hasThematicPavilion
Indicates that an entity includes or is associated with a specific thematic pavilion as part of its structure or offerings.
-
E.
hasNumberOfPalaces
Indicates the specific count of palaces associated with an entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7fef2e8c8190982dd67f50295ada |
completed | March 20, 2026, 5:12 p.m. |
| PD | Predicate disambiguation | batch_69bd77ac2fc48190abeebb003a82384c |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:43 p.m.